Bringing a novel therapeutic to market is a complex and costly journey, and toxicity remains a leading reason for failure along the way. Despite remarkable advances in drug discovery and screening technologies, unanticipated toxic effects are still responsible for high attrition rates during both preclinical and clinical development, draining time, resources, and opportunity. To remain competitive, drug developers increasingly require tools that can predict tissue-specific toxicity earlier in the pipeline, particularly in complex and sensitive organs like the retina. 

Cytotoxicity, the potential of a compound to induce cell damage or death, is a central parameter in assessing drug safety. Whether optimising the therapeutic index of a CNS active compound, evaluating delivery vectors for gene therapy, or assessing biocompatibility of new materials, cytotoxicity data is crucial. Moreover, understanding the mechanism of how cells die, whether through apoptosis, necrosis, or other regulated forms of cell death, such as ferroptosis, is equally important. These distinct mechanisms have varied implications for tissue integrity and long-term clinical outcomes. With increasing demand for more predictive and mechanistic toxicology models, researchers are shifting toward high-content, human-relevant systems that provide earlier, more detailed insight into compound behaviour.